Finally, it is worth mentioning that the here obtained NEP—valid as argued for generic transfer functions \(s_k(i_k)\) —opens the door to the potential use of more generic rate models in the field of artificial neural networks and deep learning. By identifying the NEP with the cost function to be minimized, gradient descent algorithms can be used to train networks of generic Wilson–Cowan units for different tasks. This implies that more realistic and less computationally expensive neural population models can be trained and used for behavioral tasks, a topic that has gathered attention recently [1]}, [2]}.
